class_docstr: A Linear Gaussian Bayesian Network is a Bayesian Network, all
  of whose variables are continuous, and where all of the CPDs
  are linear Gaussians.
  
  An important result is that the linear Gaussian Bayesian Networks
  are an alternative representation for the class of multivariate
  Gaussian distributions.
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docstr: Removes the cpds that are provided in the argument.
  
  Parameters
  ----------
  *cpds: LinearGaussianCPD object
      A LinearGaussianCPD object on any subset of the variables
      of the model which is to be associated with the model.
  
  Examples
  --------
  >>> from pgmpy.models import LinearGaussianBayesianNetwork
  >>> from pgmpy.factors.continuous import LinearGaussianCPD
  >>> model = LinearGaussianBayesianNetwork([('x1', 'x2'), ('x2', 'x3')])
  >>> cpd1 = LinearGaussianCPD('x1', [1], 4)
  >>> cpd2 = LinearGaussianCPD('x2', [-5, 0.5], 4, ['x1'])
  >>> cpd3 = LinearGaussianCPD('x3', [4, -1], 3, ['x2'])
  >>> model.add_cpds(cpd1, cpd2, cpd3)
  >>> for cpd in model.get_cpds():
  ...     print(cpd)
  
  P(x1) = N(1; 4)
  P(x2| x1) = N(0.5*x1_mu); -5)
  P(x3| x2) = N(-1*x2_mu); 4)
  
  >>> model.remove_cpds(cpd2, cpd3)
  >>> for cpd in model.get_cpds():
  ...     print(cpd)
  
  P(x1) = N(1; 4)
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code: def remove_cpds(self, *cpds):
      """
      Removes the cpds that are provided in the argument.
  
      Parameters
      ----------
      *cpds: LinearGaussianCPD object
          A LinearGaussianCPD object on any subset of the variables
          of the model which is to be associated with the model.
  
      Examples
      --------
      >>> from pgmpy.models import LinearGaussianBayesianNetwork
      >>> from pgmpy.factors.continuous import LinearGaussianCPD
      >>> model = LinearGaussianBayesianNetwork([('x1', 'x2'), ('x2', 'x3')])
      >>> cpd1 = LinearGaussianCPD('x1', [1], 4)
      >>> cpd2 = LinearGaussianCPD('x2', [-5, 0.5], 4, ['x1'])
      >>> cpd3 = LinearGaussianCPD('x3', [4, -1], 3, ['x2'])
      >>> model.add_cpds(cpd1, cpd2, cpd3)
      >>> for cpd in model.get_cpds():
      ...     print(cpd)
  
      P(x1) = N(1; 4)
      P(x2| x1) = N(0.5*x1_mu); -5)
      P(x3| x2) = N(-1*x2_mu); 4)
  
      >>> model.remove_cpds(cpd2, cpd3)
      >>> for cpd in model.get_cpds():
      ...     print(cpd)
  
      P(x1) = N(1; 4)
  
      """
      return super(LinearGaussianBayesianNetwork, self).remove_cpds(
          *cpds
      )
  
file_path: pgmpy/pgmpy/models/LinearGaussianBayesianNetwork.py
imports: import networkx
  import numpy
  from pgmpy.factors.continuous import LinearGaussianCPD
  from pgmpy.factors.distributions import GaussianDistribution
  from pgmpy.global_vars import logger
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 3 for calling the LinearGaussianBayesianNetwork obj's underlying member method code with example usage: obj.remove_cpds() and returns: super